





"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


SCREAMING_SNAKE_CASE__   =  logging.getLogger(__name__)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      if os.path.exists(SCREAMING_SNAKE_CASE     ):
            if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     ) and os.path.isfile(
                os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     ):
                  os.remove(os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     )
            if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE				,		"""pytorch_model.bin"""     )     ) and os.path.isfile(
                os.path.join(SCREAMING_SNAKE_CASE				,		"""pytorch_model.bin"""     )     ):
                  os.remove(os.path.join(SCREAMING_SNAKE_CASE				,		"""pytorch_model.bin"""     )     )
      else:
            os.makedirs(SCREAMING_SNAKE_CASE     )
      model.save_pretrained(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		Any=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			2
      if unlogit:
            lowerCAmelCase    		=			torch.pow(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			p * torch.log(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			0
      return -plogp.sum(dim=-1     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]     ):







      '''simple docstring'''




      logger.info("""lv, h >\t""" + """\t""".join(F'{x + 1}' for x in range(len(SCREAMING_SNAKE_CASE     )     )     )     )
      for row in range(len(SCREAMING_SNAKE_CASE     )     ):
            if tensor.dtype != torch.long:
                  logger.info(F'layer {row + 1}:\t' + """\t""".join(F'{x:.5f}' for x in tensor[row].cpu().data     )     )
            else:
                  logger.info(F'layer {row + 1}:\t' + """\t""".join(F'{x:d}' for x in tensor[row].cpu().data     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Dict=True				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]=True				,		SCREAMING_SNAKE_CASE	:		Tuple=None				,		SCREAMING_SNAKE_CASE	:		str=False     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			model.config.num_hidden_layers, model.config.num_attention_heads
      lowerCAmelCase    		=			torch.zeros(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ).to(args.device     )
      lowerCAmelCase    		=			torch.zeros(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ).to(args.device     )

      if head_mask is None:
            lowerCAmelCase    		=			torch.ones(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ).to(args.device     )

      head_mask.requires_grad_(requires_grad=SCREAMING_SNAKE_CASE     )
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
            lowerCAmelCase    		=			None

      lowerCAmelCase    		=			0.0
      lowerCAmelCase    		=			0.0
      for step, inputs in enumerate(tqdm(SCREAMING_SNAKE_CASE				,		desc="""Iteration"""				,		disable=args.local_rank not in [-1, 0]     )     ):
            lowerCAmelCase    		=			tuple(t.to(args.device     ) for t in inputs     )
            ((lowerCAmelCase)		,						)    		=			inputs

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE				,		labels=SCREAMING_SNAKE_CASE				,		head_mask=SCREAMING_SNAKE_CASE     )
            #  (loss), lm_logits, presents, (all hidden_states), (attentions)
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			(
                outputs[0],
                outputs[1],
                outputs[-1],
            )  # Loss and logits are the first, attention the last
            loss.backward()  # Backpropagate to populate the gradients in the head mask
            total_loss += loss.detach().cpu().numpy()
            if compute_entropy:
                  for layer, attn in enumerate(SCREAMING_SNAKE_CASE     ):
                        lowerCAmelCase    		=			entropy(attn.detach()				,		SCREAMING_SNAKE_CASE     )
                        attn_entropy[layer] += masked_entropy.sum(-1     ).sum(0     ).sum(0     ).detach()

            if compute_importance:
                  head_importance += head_mask.grad.abs().detach()
            tot_tokens += torch.ones_like(SCREAMING_SNAKE_CASE     ).float().detach().sum().data

      # Normalize
      attn_entropy /= tot_tokens
      head_importance /= tot_tokens
      # Layerwise importance normalization
      if not args.dont_normalize_importance_by_layer:
            lowerCAmelCase    		=			2
            lowerCAmelCase    		=			torch.pow(torch.pow(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ).sum(-1     )				,		1 / exponent     )
            head_importance /= norm_by_layer.unsqueeze(-1     ) + 1e-20

      if not args.dont_normalize_global_importance:
            lowerCAmelCase    		=			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

      # Print matrices
      if compute_entropy:
            logger.info("""Attention entropies"""     )
            print_ad_tensor(SCREAMING_SNAKE_CASE     )
      if compute_importance:
            logger.info("""Head importance scores"""     )
            print_ad_tensor(SCREAMING_SNAKE_CASE     )
      logger.info("""Head ranked by importance scores"""     )
      lowerCAmelCase    		=			torch.zeros(head_importance.numel()				,		dtype=torch.long				,		device=args.device     )
      lowerCAmelCase    		=			torch.arange(
          head_importance.numel()				,		device=args.device     )
      lowerCAmelCase    		=			head_ranks.view_as(SCREAMING_SNAKE_CASE     )
      print_ad_tensor(SCREAMING_SNAKE_CASE     )
      return attn_entropy, head_importance, total_loss


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			compute_heads_importance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		compute_entropy=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			1 / loss  # instead of downsteam score use the LM loss
      logger.info("""Pruning: original score: %f, threshold: %f"""				,		SCREAMING_SNAKE_CASE				,		original_score * args.masking_threshold     )

      lowerCAmelCase    		=			torch.ones_like(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			max(1				,		int(new_head_mask.numel() * args.masking_amount     )     )

      lowerCAmelCase    		=			original_score
      while current_score >= original_score * args.masking_threshold:
            lowerCAmelCase    		=			new_head_mask.clone().detach()  # save current head mask
            # heads from least important to most - keep only not-masked heads
            lowerCAmelCase    		=			float("""Inf"""     )
            lowerCAmelCase    		=			head_importance.view(-1     ).sort()[1]

            if len(SCREAMING_SNAKE_CASE     ) <= num_to_mask:
                  print("""BREAK BY num_to_mask"""     )
                  break

            # mask heads
            lowerCAmelCase    		=			current_heads_to_mask[:num_to_mask]
            logger.info("""Heads to mask: %s"""				,		str(current_heads_to_mask.tolist()     )     )
            lowerCAmelCase    		=			new_head_mask.view(-1     )
            lowerCAmelCase    		=			0.0
            lowerCAmelCase    		=			new_head_mask.view_as(SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			new_head_mask.clone().detach()
            print_ad_tensor(SCREAMING_SNAKE_CASE     )

            # Compute metric and head importance again
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			compute_heads_importance(
                SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		compute_entropy=SCREAMING_SNAKE_CASE				,		head_mask=SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			1 / loss
            logger.info(
                """Masking: current score: %f, remaining heads %d (%.1f percents)"""				,		SCREAMING_SNAKE_CASE				,		new_head_mask.sum()				,		new_head_mask.sum() / new_head_mask.numel() * 1_00				,		)

      logger.info("""Final head mask"""     )
      print_ad_tensor(SCREAMING_SNAKE_CASE     )
      np.save(os.path.join(args.output_dir				,		"""head_mask.npy"""     )				,		head_mask.detach().cpu().numpy()     )

      return head_mask


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Tuple				,		SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			datetime.now()
      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			compute_heads_importance(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		compute_entropy=SCREAMING_SNAKE_CASE				,		compute_importance=SCREAMING_SNAKE_CASE				,		head_mask=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			1 / loss
      lowerCAmelCase    		=			datetime.now() - before_time

      lowerCAmelCase    		=			sum(p.numel() for p in model.parameters()     )
      lowerCAmelCase    		=			{
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(SCREAMING_SNAKE_CASE     )     )
      }

      for k, v in heads_to_prune.items():
            if isinstance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ):
                  lowerCAmelCase    		=			[
                      v,
                  ]

      assert sum(len(SCREAMING_SNAKE_CASE     ) for h in heads_to_prune.values()     ) == (1 - head_mask.long()).sum().item()
      model.prune_heads(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			sum(p.numel() for p in model.parameters()     )

      lowerCAmelCase    		=			datetime.now()
      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			compute_heads_importance(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		compute_entropy=SCREAMING_SNAKE_CASE				,		compute_importance=SCREAMING_SNAKE_CASE				,		head_mask=SCREAMING_SNAKE_CASE				,		actually_pruned=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			1 / loss
      lowerCAmelCase    		=			datetime.now() - before_time

      logger.info(
          """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		pruned_num_params / original_num_params * 1_00				,		)
      logger.info("""Pruning: score with masking: %f score with pruning: %f"""				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      logger.info("""Pruning: speed ratio (original timing / new timing): %f percents"""				,		original_time / new_time * 1_00     )
      save_model(SCREAMING_SNAKE_CASE				,		args.output_dir     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--data_dir"""				,		default=SCREAMING_SNAKE_CASE				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""The input data dir. Should contain the .tsv files (or other data files) for the task."""				,		)
      parser.add_argument(
          """--model_name_or_path"""				,		default=SCREAMING_SNAKE_CASE				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""Path to pretrained model or model identifier from huggingface.co/models"""				,		)
      parser.add_argument(
          """--output_dir"""				,		default=SCREAMING_SNAKE_CASE				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""The output directory where the model predictions and checkpoints will be written."""				,		)

      # Other parameters
      parser.add_argument(
          """--config_name"""				,		default=""""""				,		type=SCREAMING_SNAKE_CASE				,		help="""Pretrained config name or path if not the same as model_name_or_path"""				,		)
      parser.add_argument(
          """--tokenizer_name"""				,		default=""""""				,		type=SCREAMING_SNAKE_CASE				,		help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""				,		)
      parser.add_argument(
          """--cache_dir"""				,		default=SCREAMING_SNAKE_CASE				,		type=SCREAMING_SNAKE_CASE				,		help="""Where do you want to store the pre-trained models downloaded from s3"""				,		)
      parser.add_argument(
          """--data_subset"""				,		type=SCREAMING_SNAKE_CASE				,		default=-1				,		help="""If > 0: limit the data to a subset of data_subset instances."""     )
      parser.add_argument(
          """--overwrite_output_dir"""				,		action="""store_true"""				,		help="""Whether to overwrite data in output directory"""     )
      parser.add_argument(
          """--overwrite_cache"""				,		action="""store_true"""				,		help="""Overwrite the cached training and evaluation sets"""     )

      parser.add_argument(
          """--dont_normalize_importance_by_layer"""				,		action="""store_true"""				,		help="""Don't normalize importance score by layers"""     )
      parser.add_argument(
          """--dont_normalize_global_importance"""				,		action="""store_true"""				,		help="""Don't normalize all importance scores between 0 and 1"""				,		)

      parser.add_argument(
          """--try_masking"""				,		action="""store_true"""				,		help="""Whether to try to mask head until a threshold of accuracy."""     )
      parser.add_argument(
          """--masking_threshold"""				,		default=0.9				,		type=SCREAMING_SNAKE_CASE				,		help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""				,		)
      parser.add_argument(
          """--masking_amount"""				,		default=0.1				,		type=SCREAMING_SNAKE_CASE				,		help="""Amount to heads to masking at each masking step."""     )
      parser.add_argument("""--metric_name"""				,		default="""acc"""				,		type=SCREAMING_SNAKE_CASE				,		help="""Metric to use for head masking."""     )

      parser.add_argument(
          """--max_seq_length"""				,		default=1_28				,		type=SCREAMING_SNAKE_CASE				,		help=(
              """The maximum total input sequence length after WordPiece tokenization. \n"""
              """Sequences longer than this will be truncated, sequences shorter padded."""
          )				,		)
      parser.add_argument("""--batch_size"""				,		default=1				,		type=SCREAMING_SNAKE_CASE				,		help="""Batch size."""     )

      parser.add_argument("""--seed"""				,		type=SCREAMING_SNAKE_CASE				,		default=42     )
      parser.add_argument("""--local_rank"""				,		type=SCREAMING_SNAKE_CASE				,		default=-1				,		help="""local_rank for distributed training on gpus"""     )
      parser.add_argument("""--no_cuda"""				,		action="""store_true"""				,		help="""Whether not to use CUDA when available"""     )
      parser.add_argument("""--server_ip"""				,		type=SCREAMING_SNAKE_CASE				,		default=""""""				,		help="""Can be used for distant debugging."""     )
      parser.add_argument("""--server_port"""				,		type=SCREAMING_SNAKE_CASE				,		default=""""""				,		help="""Can be used for distant debugging."""     )
      lowerCAmelCase    		=			parser.parse_args()

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print("""Waiting for debugger attach"""     )
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,		redirect_output=SCREAMING_SNAKE_CASE     )
            ptvsd.wait_for_attach()

      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
            lowerCAmelCase    		=			torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
            lowerCAmelCase    		=			0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank     )
            lowerCAmelCase    		=			torch.device("""cuda"""				,		args.local_rank     )
            lowerCAmelCase    		=			1
            torch.distributed.init_process_group(backend="""nccl"""     )  # Initializes the distributed backend

      # Setup logging
      logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN     )
      logger.info("""device: {} n_gpu: {}, distributed: {}""".format(args.device				,		args.n_gpu				,		bool(args.local_rank != -1     )     )     )

      lowerCAmelCase    		=			GPTaLMHeadModel.from_pretrained(args.model_name_or_path     )

      # Distributed and parallel training
      model.to(args.device     )
      if args.local_rank != -1:
            lowerCAmelCase    		=			nn.parallel.DistributedDataParallel(
                SCREAMING_SNAKE_CASE				,		device_ids=[args.local_rank]				,		output_device=args.local_rank				,		find_unused_parameters=SCREAMING_SNAKE_CASE     )
      elif args.n_gpu > 1:
            lowerCAmelCase    		=			nn.DataParallel(SCREAMING_SNAKE_CASE     )

      # Print/save training arguments
      os.makedirs(args.output_dir				,		exist_ok=SCREAMING_SNAKE_CASE     )
      torch.save(SCREAMING_SNAKE_CASE				,		os.path.join(args.output_dir				,		"""run_args.bin"""     )     )
      logger.info("""Training/evaluation parameters %s"""				,		SCREAMING_SNAKE_CASE     )

      # Prepare dataset
      lowerCAmelCase    		=			np.concatenate(
          [
              np.loadtxt(args.data_dir				,		dtype=np.intaa     ),
          ]     )
      lowerCAmelCase    		=			(torch.from_numpy(SCREAMING_SNAKE_CASE     ),)
      lowerCAmelCase    		=			TensorDataset(*SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			RandomSampler(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			DataLoader(SCREAMING_SNAKE_CASE				,		sampler=SCREAMING_SNAKE_CASE				,		batch_size=args.batch_size     )

      # Compute head entropy and importance score
      compute_heads_importance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Try head masking (set heads to zero until the score goes under a threshole)
      # and head pruning (remove masked heads and see the effect on the network)
      if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
            lowerCAmelCase    		=			mask_heads(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            prune_heads(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       main()













"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "allenai/led-base-16384": 16_384,
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = LEDTokenizer
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase="replace" ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="</s>" ,		lowercase="<s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase="<mask>" ,		lowercase=False ,		lowercase=True ,		**lowercase ,		)						->	Any:
            super().__init__(
                lowercase ,		lowercase ,		tokenizer_file=lowercase ,		errors=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		sep_token=lowercase ,		cls_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		mask_token=lowercase ,		add_prefix_space=lowercase ,		trim_offsets=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                  lowerCAmelCase    		=			getattr(lowercase ,		pre_tok_state.pop("""type"""					)					)
                  lowerCAmelCase    		=			add_prefix_space
                  lowerCAmelCase    		=			pre_tok_class(**lowercase					)

            lowerCAmelCase    		=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowerCAmelCase    		=			"""post_processor"""
            lowerCAmelCase    		=			getattr(self.backend_tokenizer ,		lowercase ,		lowercase					)
            if tokenizer_component_instance:
                  lowerCAmelCase    		=			json.loads(tokenizer_component_instance.__getstate__()					)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowerCAmelCase    		=			tuple(state["""sep"""]					)
                  if "cls" in state:
                        lowerCAmelCase    		=			tuple(state["""cls"""]					)

                  lowerCAmelCase    		=			False

                  if state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                        lowerCAmelCase    		=			add_prefix_space
                        lowerCAmelCase    		=			True

                  if state.get("""trim_offsets""" ,		lowercase					) != trim_offsets:
                        lowerCAmelCase    		=			trim_offsets
                        lowerCAmelCase    		=			True

                  if changes_to_apply:
                        lowerCAmelCase    		=			getattr(lowercase ,		state.pop("""type"""					)					)
                        lowerCAmelCase    		=			component_class(**lowercase					)
                        setattr(self.backend_tokenizer ,		lowercase ,		lowercase					)





      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def        _snake_case     (			self					)						->	str:
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""					)
                  return None
            return str(self._mask_token					)





      @mask_token.setter
      def        _snake_case     (			self ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else value
            lowerCAmelCase    		=			value





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._batch_encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            lowerCAmelCase    		=			self._tokenizer.model.save(lowercase ,		name=lowercase					)
            return tuple(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase=None					)						->	Any:
            lowerCAmelCase    		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = PaddingStrategy.DO_NOT_PAD ,		lowercase = None ,		lowercase = None ,		)						->	dict:
            lowerCAmelCase    		=			super()._pad(
                encoded_inputs=lowercase ,		max_length=lowercase ,		padding_strategy=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=lowercase ,		)

            # Load from model defaults
            if return_attention_mask is None:
                  lowerCAmelCase    		=			"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  lowerCAmelCase    		=			encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  lowerCAmelCase    		=			len(encoded_inputs["""global_attention_mask"""]					) != len(lowercase					)

                  if needs_to_be_padded:
                        lowerCAmelCase    		=			len(lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

                        if self.padding_side == "right":
                              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                              lowerCAmelCase    		=			(
                                  encoded_inputs["""global_attention_mask"""] + [-1] * difference
                              )
                        elif self.padding_side == "left":
                              lowerCAmelCase    		=			[-1] * difference + encoded_inputs[
                                  """global_attention_mask"""
                              ]
                        else:
                              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs






